1 Introduction

As the coronavirus spread in Philadelphia this year, travel patterns changed. With this shock to human mobility came another to business activity, as far fewer of us traveled to work, amenities or other pastimes. In order to understand the consequences of these changes, we use GPS data from mobile phones explore how Philadelphians changed their travel patterns before and during the pandemic.

With the goal of understanding the time-space patterns of resident movement in Philadelphia, the following section presents data from SafeGraph, a provider of mobility data collected from mobile devices. Note that SafeGraph collects data on a representative sample (10%) of the population across the country, so our indicators are not the true number of visits or journeys, but a slice. The data contain the terms defined in Figure 1.1: the number of visitors is the count of devices flowing to a point of interest (POI) while a connection is an origin-destination line between a Census Block Group and a point of interest, regardless of its weight. A flow is also a connection, but with a weight measuring the number of visitors traveling between origin and destination.

1.1 Key definitions

Each visit is a mobile device entering into a point of interest; these include parks and museums, restaurants and bars, or offices and hospitals. In figure 1.3 we map the distribution of these venues and businesses for context. We classify each point of interest by its description, which SafeGraph provides. 1 We can see that most businesses cluster in Center City or nearby but no businesses cluster more than restaurants and bars.

1.3 Distribution of points of interest by class

To demonstrate how this information combines to form a network, Figure 1.2 connects origins to destinations by month in Philadelphia. This network is what we will be exploring below.

1.2 Origin-destination flows over time

This analysis comprises different spatial scales, Citywide, Neighborhood and Point of Interest. We can look globally, across the city, to explore trends throughout; we can also think locally, dividing the city up into cells or neighborhoods to probe variations within the city. Finally, we can look at individual businesses or venues. Below, we attempt to understand patterns at each scale.

2 Citywide analysis

In this section we explore trends and relationships manifest most strongly citywide. To see how the business environment is for chains across the city, rather than any given location, we sum visits by brand. Figure 2.1 shows that brands deemed essential businesses saw comparably less of a decline than others, along with fast food restaurants. The map shows the locations of brands for context.

2.1 Relative brand performance (January-August)

Rankings Locations

Figure 2.2 ranks each brand by the number of visitors it received and animates this change through the pandemic. Dollar stores rise gradually throughout the year; another important shift is away from non-essential retail towards essential businesses like pharmacies. Starbucks and Wawa occupy top spots for the first several weeks of the year but when the shelter-in-place order sets in, visits collapse and they are replaced in the ranks by essentials RiteAid and ShopRite.

2.2 Comparing traffic by brand (January-August)

In Figure 2.1 we aggregate by use, grouping by classes like leisure (restaurants and bars) and tourism (museums and theaters). The pandemic had curbed visits to each class of business, but particularly leisure and other, which includes office spaces. Interestingly, tourism is recovering while shopping and grocers are not, perhaps as many switch to digital commerce.

2.4 Did trip reductions change with respect to income?

In March, at the onset of the pandemic, more Philadelphians were leaving their home tracts. In April, all communties see reductions in trips. However, the higher the median income, the greater the reduction in trips. The reverse is true for race: the higher the minority population, the greater the reduction of trips, but the relationship diminishes during the summer.

2.5 Did trip reductions change with respect to race?

Next we look at how mobilities varies across neighborhoods and commercial corridors to determine whether or not pandemic is impacting some parts of the city more than others.

3 Neighborhood variation

In this section, we present trends across neighborhoods and other smaller geographies. These allow us to see how visits in particular are changing in different parts of Philadelphia. We find large disparities between the best and worst performing regions.

We explore trends across neighborhoods in Figure 3.1; neighborhoods dominated by office work, like the Navy Yard along with Logan Square and Center City, saw precipitous declines in foot traffic, but those with strong amenities and residential communities have recovered. This suggests that demand for food, drink, and shopping may be shifting away from the core. (Note: see the appendix for larger tables.)

3.2 Visits by month in gridded units

Businesses cluster together and we can explore the strength of this phenomenon by looking at commercial corridors, of which the city has designated roughly 280. Looking at night life in figure 3.3, the largest are Market West and Market East, on either side of city hall, with 1712 and 1263 restaurants and bars respectively, following by Old City at 654 and another in University City with 493: most of the business activity is concentrated in a few locales.

3.3 Commercial corridors

When we plot trends in these clusters over time, it is clear that many of the most successful areas are toward the periphery, perhaps dormitory communities supported by remote work, and several of the least successful are situated in the core. Notably among the worst performers are the two central corridors, which depend on office work, and the Sports Complex, which saw sports leagues take measures of protect players and ban fans early on—and many of these restrictions are still in place. Peripheral plazas like Oxford and Levick, home to a supermarket, and City and Haverford are among the best.

4 Points of Interest in focus

This section looks at individual points of interest, how they perform over time and whether or not we can identify certain bellwether businesses within the city. These cases can provide further insight into how the pandemic is changing mobility. We start by looking at the network of connections across the city. Drawing a line between each origin (neighborhood) and destination (point of interest), there is a dense web—a nearly saturated graph where all neighborhoods send visitors to all other corners of the city. This web becomes sparser as the pandemic came to the fore and there during the late summer there were fewer links than during the late winter.

4.1 Aggregate mobility

As we saw above, the data show that big box stores like Target and Walmart appear to have weathered the pandemic well, but the shift to remote work should also appear in the data. We can look at visits to the Comcast Center and the Plaza below it; visits in April and May, as the coronavirus took hold in the city, fell substantially.

4.2 Business in focus

Yet with offices vacant, parks should have swelled with visitors. We see mixed evidence of this in the data. Philadelphia has four central squares—Rittenhouse, Washington, Logan, and Franklin—which provide important community amenity; all saw fewer visits in April and May than later in the summer, suggesting winter patterns continued even as the weather improved. As a signal for tourism, we can look at Reading Terminal Market; vendors between its walls saw marked declines in visits beginning in April.

4.3 Tourism in focus

Appendix

A.1 Expanding neighborhood ranks

Best Worst

A.2 Explanatory variables

A.3 Expanding corridor ranks

Best Worst

A.4 Visits by race and income


  1. If that description contains “restaurant” or “bar”, we classify that as leisure. Anything educational, from tutoring to public, private or charter schools to tertiary education, we call that education. Tourism includes museums and parks.